Active Segmentation on FENS 2018

INCF hosted a booth with community demos at FENS Forum 2018. The Active Segmentation plugin was presented there by Sumit Vohra, the main developer of the platform, who was funded by GSOC 2016. Sumit developed the initial version of platform as pare of the master thesis project with Dimiter Prodanov, a Fellow of the Belgian INCF Node.

The Active Segmentation aims of providing a general purpose workbench that would allow biologists to access state-of-the-art techniques in machine learning and image processing to improve their image segmentation results. The major goal of the platform is to provide a well documented tool for users along with visual insights so that user can understand the underlined methodology

Currently, Active Segmentation supports various geometric features, such as Laplace of Gaussian , Gaussian Derivatives among others. It also provide various machine learning algorithms like Support Vector machine, Naive Bayes etc available in WEKA library. The platform allows researchers to add their own feature extraction and machine learning algorithms.